Towards Training-free Automatic Proxy Discovery via Large Language Models for Mixed Precision Quantization
This paper proposes a novel Large Language Model (LLM)-driven Training-free Automatic Proxy (dubbed TAP) discovery framework that reforms the design paradigm of MPQ by utilizing LLMs and evolutionary search strategies to automatically find superior TAP tailored for MPQ.
Hai-Dong Kang, Jun Du, Guo Yu
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